Results:We present two algorithms for inclusion list creation that formulate precursor ion selection as an optimization problem. Given an LC-MS map, the first approach maximizes the number of selected precursors given constraints such as a limited number of acquisitions per RT fraction. Second, we introduce a protein sequence-based inclusion list that can be used to monitor proteins of interest. Given only the protein sequences, we create an inclusion list that optimally covers the whole protein set. Additionally, we propose an iterative precursor ion selection that aims at reducing the redundancy obtained with data dependent LC-MS/MS. We overcome the risk of erroneous assignments by including methods for retention time and proteotypicity predictions. We show that our method identifies a set of proteins requiring fewer precursors than standard approaches. Thus, it is well suited for precursor ion selection in experiments with limited sample amount or analysis time.

Conclusions:We present three approaches to precursor ion selection with LC-MALDI MS/MS. Using a well defined protein standard and a complex human cell lysate, we demonstrate that our methods out perform standard approaches. Our algorithms are implemented as part of OpenMS and are available under www.openms.de.

The article is published online in the journal BMC Bioinformatics and is free to access.

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